Java 类名:com.alibaba.alink.operator.batch.graph.Node2VecBatchOp
Python 类名:Node2VecBatchOp
功能介绍
node2vec是一种用于网络中的特征学习有效的可扩展算法,该算法可以使用SGD有效地优化,能根据网络中的既定原则,为发现符合不同等值的表示提供了灵活性
node2vec: Scalable Feature Learning for Networks
参数说明
| 名称 | 中文名称 | 描述 | 类型 | 是否必须? | 取值范围 | 默认值 | | —- | —- | —- | —- | —- | —- | —- |
| sourceCol | 起始点列名 | 用来指定起始点列 | String | ✓ | | |
| targetCol | 中止点点列名 | 用来指定中止点列 | String | ✓ | | |
| walkLength | 游走的长度 | 随机游走完向量的长度 | Integer | ✓ | | |
| walkNum | 路径数目 | 每一个起始点游走出多少条路径 | Integer | ✓ | | |
| alpha | 学习率 | 学习率 | Double | | | 0.025 |
| batchSize | batch大小 | batch大小, 按行计算 | Integer | | [1, +inf) | |
| isToUndigraph | 是否转无向图 | 选为true时,会将当前图转成无向图,然后再游走 | Boolean | | | false |
| minCount | 最小词频 | 最小词频 | Integer | | | 5 |
| negative | 负采样大小 | 负采样大小 | Integer | | | 5 |
| numIter | 迭代次数 | 迭代次数,默认为1。 | Integer | | | 1 |
| p | p | p越小越趋向于访问到已经访问的节点,反之则趋向于访问没有访问过的节点 | Double | | | 1.0 |
| q | q | q>1时行为类似于bfs趋向于访问和访问过的节点相连的节点,q<1时行为类似于dfs | Double | | | 1.0 |
| randomWindow | 是否使用随机窗口 | 是否使用随机窗口,默认使用 | String | | | “true” |
| vectorSize | embedding的向量长度 | embedding的向量长度 | Integer | | [1, +inf) | 100 |
| weightCol | 权重列名 | 权重列对应的列名 | String | | 所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT] | null |
| window | 窗口大小 | 窗口大小 | Integer | | | 5 |
| wordDelimiter | 单词分隔符 | 单词之间的分隔符 | String | | | “ “ |
代码示例
Python 代码
from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
["Bob", "Lucy", 1.],
["Lucy", "Bob", 1.],
["Lucy", "Bella", 1.],
["Bella", "Lucy", 1.],
["Alice", "Lisa", 1.],
["Lisa", "Alice", 1.],
["Lisa", "Karry", 1.],
["Karry", "Lisa", 1.],
["Karry", "Bella", 1.],
["Bella", "Karry", 1.]
])
source = BatchOperator.fromDataframe(df, schemaStr="start string, end string, value double")
node2vecBatchOp = Node2VecBatchOp() \
.setSourceCol("start") \
.setTargetCol("end") \
.setWeightCol("value") \
.setWalkNum(2) \
.setWalkLength(2) \
.setMinCount(1) \
.setVectorSize(4)
node2vecBatchOp.linkFrom(source).print()
运行结果
| node | vec | | —- | —- |
| Karry | 0.02435881271958351,0.0703350380063057,-0.04173225536942482,-0.06183897703886032 |
| Bella | -0.028720347210764885,0.02828666940331459,0.12123052030801773,0.12075022608041763 |
| Alice | 0.03435942903161049,-0.04773801192641258,0.0125938905403018,-0.09576953202486038 |
| Lisa | -0.07306616753339767,-0.11595576256513596,-0.04181118682026863,0.03970039263367653 |
| Bob | 0.0577755942940712,0.08282522112131119,-0.06487344205379486,0.026600968092679977 |
| Lucy | 0.057738181203603745,-0.09987597167491913,-0.022486409172415733,-0.02312176302075386 |